
'''simple docstring'''






from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


UpperCAmelCase_							: str =							logging.get_logger(__name__)

class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		['input_features', 'attention_mask']



    def __init__(						self   :							Any,__A   :							List[Any]=8_0,__A   :							Dict=1_6_0_0_0,__A   :							Tuple=0.0,__A   :							Dict=1_0,__A   :							int=2_5,__A   :							Union[str, Any]="hamming_window",__A   :							List[str]=32768.0,__A   :							Union[str, Any]=0.97,__A   :							str=1.0,__A   :							Union[str, Any]=True,__A   :							Tuple=True,__A   :							Optional[Any]=False,**__A   :							Optional[Any],):
          super().__init__(feature_size=__A,sampling_rate=__A,padding_value=__A,**__A  )

          _lowerCamelCase					: Dict		    = feature_size
          _lowerCamelCase					: List[str]		    = sampling_rate
          _lowerCamelCase					: Any		    = padding_value
          _lowerCamelCase					: Dict		    = hop_length
          _lowerCamelCase					: Tuple		    = win_length
          _lowerCamelCase					: str		    = frame_signal_scale
          _lowerCamelCase					: List[str]		    = preemphasis_coeff
          _lowerCamelCase					: List[str]		    = mel_floor
          _lowerCamelCase					: str		    = normalize_means
          _lowerCamelCase					: Any		    = normalize_vars
          _lowerCamelCase					: List[str]		    = win_function
          _lowerCamelCase					: Tuple		    = return_attention_mask

          _lowerCamelCase					: List[Any]		    = win_length * sampling_rate // 1_0_0_0
          _lowerCamelCase					: List[Any]		    = hop_length * sampling_rate // 1_0_0_0

          _lowerCamelCase					: Any		    = optimal_fft_length(self.sample_size  )
          _lowerCamelCase					: Dict		    = (self.n_fft // 2) + 1



    def 	lowerCamelCase_	(						self   :							Any,__A   :							np.array  ):
          if self.win_function == "hamming_window":
                _lowerCamelCase					: Any		    = window_function(window_length=self.sample_size,name=self.win_function,periodic=__A  )
          else:
                _lowerCamelCase					: Optional[int]		    = window_function(window_length=self.sample_size,name=self.win_function  )

          _lowerCamelCase					: int		    = mel_filter_bank(
              num_frequency_bins=self.n_freqs,num_mel_filters=self.feature_size,min_frequency=0.0,max_frequency=self.sampling_rate / 2.0,sampling_rate=self.sampling_rate,)

          _lowerCamelCase					: List[str]		    = spectrogram(
              one_waveform * self.frame_signal_scale,window=__A,frame_length=self.sample_size,hop_length=self.sample_stride,fft_length=self.n_fft,center=__A,preemphasis=self.preemphasis_coeff,mel_filters=__A,mel_floor=self.mel_floor,log_mel="log",)
          return msfc_features.T



    def 	lowerCamelCase_	(						self   :							Optional[int],__A   :							List[str],__A   :							Dict,__A   :							int  ):
          # make sure we normalize float32 arrays
          if self.normalize_means:
                _lowerCamelCase					: Optional[Any]		    = x[:input_length].mean(axis=0  )
                _lowerCamelCase					: Optional[int]		    = np.subtract(__A,__A  )
          if self.normalize_vars:
                _lowerCamelCase					: int		    = x[:input_length].std(axis=0  )
                _lowerCamelCase					: Any		    = np.divide(__A,__A  )

          if input_length < x.shape[0]:
                _lowerCamelCase					: Tuple		    = padding_value

          # make sure array is in float32
          _lowerCamelCase					: Optional[int]		    = x.astype(np.floataa  )

          return x



    def 	lowerCamelCase_	(						self   :							Any,__A   :							List[np.ndarray],__A   :							Optional[np.ndarray] = None  ):
          _lowerCamelCase					: Optional[int]		    = attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
          return [self._normalize_one(__A,__A,self.padding_value  ) for x, n in zip(__A,__A  )]



    def __call__(						self   :							Optional[Any],__A   :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],__A   :							Union[bool, str, PaddingStrategy] = False,__A   :							Optional[int] = None,__A   :							bool = False,__A   :							Optional[int] = None,__A   :							Optional[bool] = None,__A   :							Optional[Union[str, TensorType]] = None,__A   :							Optional[int] = None,**__A   :							Optional[Any],):

          if sampling_rate is not None:
                if sampling_rate != self.sampling_rate:
                      raise ValueError(
                          f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                          f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                          f' {self.sampling_rate} and not {sampling_rate}.'  )
          else:
                logger.warning(
                    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
                    "Failing to do so can result in silent errors that might be hard to debug."  )

          _lowerCamelCase					: List[str]		    = isinstance(__A,np.ndarray  ) and len(raw_speech.shape  ) > 1
          if is_batched_numpy and len(raw_speech.shape  ) > 2:
                raise ValueError(f'Only mono-channel audio is supported for input to {self}'  )
          _lowerCamelCase					: List[str]		    = is_batched_numpy or (
              isinstance(__A,(list, tuple)  ) and (isinstance(raw_speech[0],(np.ndarray, tuple, list)  ))
          )

          if is_batched:
                _lowerCamelCase					: List[Any]		    = [np.asarray(__A,dtype=np.floataa  ) for speech in raw_speech]
          elif not is_batched and not isinstance(__A,np.ndarray  ):
                _lowerCamelCase					: Dict		    = np.asarray(__A,dtype=np.floataa  )
          elif isinstance(__A,np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
                _lowerCamelCase					: Tuple		    = raw_speech.astype(np.floataa  )

          # always return batch
          if not is_batched:
                _lowerCamelCase					: Tuple		    = [raw_speech]

          # extract fbank features
          _lowerCamelCase					: str		    = [self._extract_mfsc_features(__A  ) for one_waveform in raw_speech]

          # convert into correct format for padding
          _lowerCamelCase					: Union[str, Any]		    = BatchFeature({"input_features": features}  )

          _lowerCamelCase					: List[Any]		    = self.pad(
              __A,padding=__A,max_length=__A,truncation=__A,pad_to_multiple_of=__A,return_attention_mask=__A,**__A,)
          # make sure list is in array format
          _lowerCamelCase					: Optional[Any]		    = padded_inputs.get("input_features"  )
          if isinstance(input_features[0],__A  ):
                _lowerCamelCase					: int		    = [np.asarray(__A,dtype=np.floataa  ) for feature in input_features]

          _lowerCamelCase					: Dict		    = padded_inputs.get("attention_mask"  )
          if attention_mask is not None:
                _lowerCamelCase					: Dict		    = [np.asarray(__A,dtype=np.intaa  ) for array in attention_mask]

          if self.normalize_means or self.normalize_vars:
                _lowerCamelCase					: Dict		    = (
                    np.array(__A,dtype=np.intaa  )
                    if self._get_padding_strategies(__A,max_length=__A  ) is not PaddingStrategy.DO_NOT_PAD
                    and padding
                    else None
                )
                _lowerCamelCase					: Tuple		    = self.normalize(
                    padded_inputs["input_features"],attention_mask=__A  )

          if return_tensors is not None:
                _lowerCamelCase					: Dict		    = padded_inputs.convert_to_tensors(__A  )

          return padded_inputs







"""simple docstring"""
import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case : Optional[Any]     		=		"""."""

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
snake_case : List[str]     		=		[
    """Assert""",
    """AssignVariableOp""",
    """EmptyTensorList""",
    """MergeV2Checkpoints""",
    """ReadVariableOp""",
    """ResourceGather""",
    """RestoreV2""",
    """SaveV2""",
    """ShardedFilename""",
    """StatefulPartitionedCall""",
    """StaticRegexFullMatch""",
    """VarHandleOp""",
]


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: Tuple ,     __snake_case: Any  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    SavedModel()
       __magic_name__      				=    []

       with open(os.path.join(__snake_case ,     'utils' ,     'tf_ops' ,     'onnx.json'  )  ) as f:
              __magic_name__      				=    json.load(__snake_case  )['opsets']

       for i in range(1 ,     opset + 1  ):
              onnx_ops.extend(onnx_opsets[str(__snake_case  )]  )

       with open(__snake_case ,     'rb'  ) as f:
              saved_model.ParseFromString(f.read()  )

       __magic_name__      				=    set()

       # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
       for meta_graph in saved_model.meta_graphs:
              # Add operations in the graph definition
              model_op_names.update(node.op for node in meta_graph.graph_def.node  )

              # Go through the functions in the graph definition
              for func in meta_graph.graph_def.library.function:
                     # Add operations in each function
                     model_op_names.update(node.op for node in func.node_def  )

    # Convert to list, sorted if you want
       __magic_name__      				=    sorted(__snake_case  )
       __magic_name__      				=    []

       for op in model_op_names:
              if op not in onnx_ops and op not in INTERNAL_OPS:
                     incompatible_ops.append(__snake_case  )

       if strict and len(__snake_case  ) > 0:
              raise Exception(F"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops  )
       elif len(__snake_case  ) > 0:
              print(F"""Found the following incompatible ops for the opset {opset}:"""  )
              print(*__snake_case ,     sep='\n'  )
       else:
              print(F"""The saved model {saved_model_path} can properly be converted with ONNX."""  )


if __name__ == "__main__":
 snake_case : Optional[Any]     		=		argparse.ArgumentParser()
 parser.add_argument("""--saved_model_path""", help="""Path of the saved model to check (the .pb file).""")
 parser.add_argument(
     """--opset""", default=1_2, type=int, help="""The ONNX opset against which the model has to be tested."""
 )
 parser.add_argument(
     """--framework""", choices=["""onnx"""], default="""onnx""", help="""Frameworks against which to test the saved model."""
 )
 parser.add_argument(
     """--strict""", action="""store_true""", help="""Whether make the checking strict (raise errors) or not (raise warnings)"""
 )
 snake_case : Any     		=		parser.parse_args()

 if args.framework == "onnx":
  onnx_compliancy(args.saved_model_path, args.strict, args.opset)